AlkantarClanX12
Your IP : 3.145.56.150
######################## BEGIN LICENSE BLOCK ########################
# The Original Code is Mozilla Universal charset detector code.
#
# The Initial Developer of the Original Code is
# Netscape Communications Corporation.
# Portions created by the Initial Developer are Copyright (C) 2001
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# Mark Pilgrim - port to Python
# Shy Shalom - original C code
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
# 02110-1301 USA
######################### END LICENSE BLOCK #########################
from typing import Dict, List, NamedTuple, Optional, Union
from .charsetprober import CharSetProber
from .enums import CharacterCategory, ProbingState, SequenceLikelihood
class SingleByteCharSetModel(NamedTuple):
charset_name: str
language: str
char_to_order_map: Dict[int, int]
language_model: Dict[int, Dict[int, int]]
typical_positive_ratio: float
keep_ascii_letters: bool
alphabet: str
class SingleByteCharSetProber(CharSetProber):
SAMPLE_SIZE = 64
SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
POSITIVE_SHORTCUT_THRESHOLD = 0.95
NEGATIVE_SHORTCUT_THRESHOLD = 0.05
def __init__(
self,
model: SingleByteCharSetModel,
is_reversed: bool = False,
name_prober: Optional[CharSetProber] = None,
) -> None:
super().__init__()
self._model = model
# TRUE if we need to reverse every pair in the model lookup
self._reversed = is_reversed
# Optional auxiliary prober for name decision
self._name_prober = name_prober
self._last_order = 255
self._seq_counters: List[int] = []
self._total_seqs = 0
self._total_char = 0
self._control_char = 0
self._freq_char = 0
self.reset()
def reset(self) -> None:
super().reset()
# char order of last character
self._last_order = 255
self._seq_counters = [0] * SequenceLikelihood.get_num_categories()
self._total_seqs = 0
self._total_char = 0
self._control_char = 0
# characters that fall in our sampling range
self._freq_char = 0
@property
def charset_name(self) -> Optional[str]:
if self._name_prober:
return self._name_prober.charset_name
return self._model.charset_name
@property
def language(self) -> Optional[str]:
if self._name_prober:
return self._name_prober.language
return self._model.language
def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
# TODO: Make filter_international_words keep things in self.alphabet
if not self._model.keep_ascii_letters:
byte_str = self.filter_international_words(byte_str)
else:
byte_str = self.remove_xml_tags(byte_str)
if not byte_str:
return self.state
char_to_order_map = self._model.char_to_order_map
language_model = self._model.language_model
for char in byte_str:
order = char_to_order_map.get(char, CharacterCategory.UNDEFINED)
# XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but
# CharacterCategory.SYMBOL is actually 253, so we use CONTROL
# to make it closer to the original intent. The only difference
# is whether or not we count digits and control characters for
# _total_char purposes.
if order < CharacterCategory.CONTROL:
self._total_char += 1
if order < self.SAMPLE_SIZE:
self._freq_char += 1
if self._last_order < self.SAMPLE_SIZE:
self._total_seqs += 1
if not self._reversed:
lm_cat = language_model[self._last_order][order]
else:
lm_cat = language_model[order][self._last_order]
self._seq_counters[lm_cat] += 1
self._last_order = order
charset_name = self._model.charset_name
if self.state == ProbingState.DETECTING:
if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
confidence = self.get_confidence()
if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
self.logger.debug(
"%s confidence = %s, we have a winner", charset_name, confidence
)
self._state = ProbingState.FOUND_IT
elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
self.logger.debug(
"%s confidence = %s, below negative shortcut threshold %s",
charset_name,
confidence,
self.NEGATIVE_SHORTCUT_THRESHOLD,
)
self._state = ProbingState.NOT_ME
return self.state
def get_confidence(self) -> float:
r = 0.01
if self._total_seqs > 0:
r = (
(
self._seq_counters[SequenceLikelihood.POSITIVE]
+ 0.25 * self._seq_counters[SequenceLikelihood.LIKELY]
)
/ self._total_seqs
/ self._model.typical_positive_ratio
)
# The more control characters (proportionnaly to the size
# of the text), the less confident we become in the current
# charset.
r = r * (self._total_char - self._control_char) / self._total_char
r = r * self._freq_char / self._total_char
if r >= 1.0:
r = 0.99
return r